Library prosa.results.edf.rta.bounded_pi

Require Export prosa.analysis.facts.edf.
Require Export prosa.model.schedule.priority_driven.
Require Export prosa.analysis.facts.busy_interval.carry_in.
Require Export prosa.analysis.definitions.schedulability.
Require Import prosa.model.priority.edf.
Require Import prosa.model.task.absolute_deadline.
Require Import prosa.analysis.abstract.ideal_jlfp_rta.
From mathcomp Require Import ssreflect ssrbool eqtype ssrnat seq path fintype bigop.

Throughout this file, we assume ideal uni-processor schedules.
Require Import prosa.model.processor.ideal.

Throughout this file, we assume the basic (i.e., Liu & Layland) readiness model.
Require Import prosa.model.readiness.basic.

Abstract RTA for EDF-schedulers with Bounded Priority Inversion

In this module we instantiate the Abstract Response-Time analysis (aRTA) to EDF-schedulers for ideal uni-processor model of real-time tasks with arbitrary arrival models.
Given EDF priority policy and an ideal uni-processor scheduler model, we can explicitly specify interference, interfering_workload, and interference_bound_function. In this settings, we can define natural notions of service, workload, busy interval, etc. The important feature of this instantiation is that we can induce the meaningful notion of priority inversion. However, we do not specify the exact cause of priority inversion (as there may be different reasons for this, like execution of a non-preemptive segment or blocking due to resource locking). We only assume that that a priority inversion is bounded.

Section AbstractRTAforEDFwithArrivalCurves.

Consider any type of tasks ...
  Context {Task : TaskType}.
  Context `{TaskCost Task}.
  Context `{TaskDeadline Task}.
  Context `{TaskRunToCompletionThreshold Task}.

... and any type of jobs associated with these tasks.
  Context {Job : JobType}.
  Context `{JobTask Job Task}.
  Context `{JobArrival Job}.
  Context `{JobCost Job}.
  Context `{JobPreemptable Job}.

For clarity, let's denote the relative deadline of a task as D.
  Let D tsk := task_deadline tsk.

Consider the EDF policy that indicates a higher-or-equal priority relation. Note that we do not relate the EDF policy with the scheduler. However, we define functions for Interference and Interfering Workload that actively use the concept of priorities.
  Let EDF := EDF Job.

Consider any arrival sequence with consistent, non-duplicate arrivals.
  Variable arr_seq : arrival_sequence Job.
  Hypothesis H_arrival_times_are_consistent : consistent_arrival_times arr_seq.
  Hypothesis H_arr_seq_is_a_set : arrival_sequence_uniq arr_seq.

Next, consider any ideal uniprocessor schedule of this arrival sequence ...
  Variable sched : schedule (ideal.processor_state Job).
  Hypothesis H_jobs_come_from_arrival_sequence:
    jobs_come_from_arrival_sequence sched arr_seq.

... where jobs do not execute before their arrival or after completion.
  Hypothesis H_jobs_must_arrive_to_execute : jobs_must_arrive_to_execute sched.
  Hypothesis H_completed_jobs_dont_execute : completed_jobs_dont_execute sched.

Note that we differentiate between abstract and classical notions of work conserving schedule.
  Let work_conserving_ab := definitions.work_conserving arr_seq sched.
  Let work_conserving_cl := work_conserving.work_conserving arr_seq sched.

We assume that the schedule is a work-conserving schedule in the classical sense, and later prove that the hypothesis about abstract work-conservation also holds.
  Hypothesis H_work_conserving : work_conserving_cl.

Assume we have sequential tasks, i.e, jobs from the same task execute in the order of their arrival.
  Hypothesis H_sequential_tasks : sequential_tasks sched.

Assume that a job cost cannot be larger than a task cost.
  Hypothesis H_valid_job_cost:
    arrivals_have_valid_job_costs arr_seq.

Consider an arbitrary task set ts.
  Variable ts : list Task.

Next, we assume that all jobs come from the task set.
  Hypothesis H_all_jobs_from_taskset : all_jobs_from_taskset arr_seq ts.

Let max_arrivals be a family of valid arrival curves, i.e., for any task tsk in ts max_arrival tsk is (1) an arrival bound of tsk, and (2) it is a monotonic function that equals 0 for the empty interval delta = 0.
  Context `{MaxArrivals Task}.
  Hypothesis H_valid_arrival_curve : valid_taskset_arrival_curve ts max_arrivals.
  Hypothesis H_is_arrival_curve : taskset_respects_max_arrivals arr_seq ts.

Let tsk be any task in ts that is to be analyzed.
  Variable tsk : Task.
  Hypothesis H_tsk_in_ts : tsk \in ts.

Consider a valid preemption model...
  Hypothesis H_valid_preemption_model:
    valid_preemption_model arr_seq sched.

...and a valid task run-to-completion threshold function. That is, task_run_to_completion_threshold tsk is (1) no bigger than tsk's cost, (2) for any job of task tsk job_run_to_completion_threshold is bounded by task_run_to_completion_threshold.
  Hypothesis H_valid_run_to_completion_threshold:
    valid_task_run_to_completion_threshold arr_seq tsk.

We introduce rbf as an abbreviation of the task request bound function, which is defined as task_cost(T) × max_arrivals(T,Δ) for some task T.
  Let rbf := task_request_bound_function.

Next, we introduce task_rbf as an abbreviation of the task request bound function of task tsk.
  Let task_rbf := rbf tsk.

Using the sum of individual request bound functions, we define the request bound function of all tasks (total request bound function).
  Let total_rbf := total_request_bound_function ts.

For simplicity, let's define some local names.
  Let response_time_bounded_by := task_response_time_bound arr_seq sched.
  Let number_of_task_arrivals := number_of_task_arrivals arr_seq.

Assume that there exists a constant priority_inversion_bound that bounds the length of any priority inversion experienced by any job of tsk. Since we analyze only task tsk, we ignore the lengths of priority inversions incurred by any other tasks.
  Variable priority_inversion_bound : duration.
  Hypothesis H_priority_inversion_is_bounded:
    priority_inversion_is_bounded_by
      arr_seq sched tsk priority_inversion_bound.

Let L be any positive fixed point of the busy interval recurrence.
  Variable L : duration.
  Hypothesis H_L_positive : L > 0.
  Hypothesis H_fixed_point : L = total_rbf L.

Next, we define an upper bound on interfering workload received from jobs of other tasks with higher-than-or-equal priority.
  Let bound_on_total_hep_workload (A Δ : duration) :=
    \sum_(tsk_o <- ts | tsk_o != tsk)
     rbf tsk_o (minn ((A + ε) + D tsk - D tsk_o) Δ).

To reduce the time complexity of the analysis, we introduce the notion of search space for EDF. Intuitively, this corresponds to all "interesting" arrival offsets that the job under analysis might have with regard to the beginning of its busy-window.
In case of search space for EDF we ask whether task_rbf A ≠ task_rbf (A + ε)...
  Definition task_rbf_changes_at (A : duration) := task_rbf A != task_rbf (A + ε).

...or there exists a task tsko from ts such that tsko ≠ tsk and rbf(tsko, A + D tsk - D tsko) ≠ rbf(tsko, A + ε + D tsk - D tsko). Note that we use a slightly uncommon notation has (λ tsko ⇒ P tskₒ) ts which can be interpreted as follows: task-set ts contains a task tsko such that a predicate P holds for tsko.
  Definition bound_on_total_hep_workload_changes_at A :=
    has (fun tsko ⇒
           (tsk != tsko)
             && (rbf tsko (A + D tsk - D tsko)
                     != rbf tsko ((A + ε) + D tsk - D tsko))) ts.

The final search space for EDF is a set of offsets that are less than L and where task_rbf or bound_on_total_hep_workload changes.
  Let is_in_search_space (A : duration) :=
    (A < L) && (task_rbf_changes_at A || bound_on_total_hep_workload_changes_at A).

Let R be a value that upper-bounds the solution of each response-time recurrence, i.e., for any relative arrival time A in the search space, there exists a corresponding solution F such that F + (task cost - task lock-in service) ≤ R.
  Variable R : duration.
  Hypothesis H_R_is_maximum:
    ∀ (A : duration),
      is_in_search_space A →
      ∃ (F : duration),
        A + F = priority_inversion_bound
                + (task_rbf (A + ε) - (task_cost tsk - task_run_to_completion_threshold tsk))
                + bound_on_total_hep_workload A (A + F) ∧
        F + (task_cost tsk - task_run_to_completion_threshold tsk) ≤ R.


To use the theorem uniprocessor_response_time_bound_seq from the Abstract RTA module, we need to specify functions of interference, interfering workload and IBF.
Instantiation of Interference We say that job j incurs interference at time t iff it cannot execute due to a higher-or-equal-priority job being scheduled, or if it incurs a priority inversion.
  Let interference (j : Job) (t : instant) :=
    ideal_jlfp_rta.interference sched j t.

Instantiation of Interfering Workload The interfering workload, in turn, is defined as the sum of the priority inversion function and interfering workload of jobs with higher or equal priority.
  Let interfering_workload (j : Job) (t : instant) :=
    ideal_jlfp_rta.interfering_workload arr_seq sched j t.

Finally, we define the interference bound function as the sum of the priority interference bound and the higher-or-equal-priority workload.
  Let IBF (A R : duration) := priority_inversion_bound + bound_on_total_hep_workload A R.

Filling Out Hypothesis Of Abstract RTA Theorem

In this section we prove that all hypotheses necessary to use the abstract theorem are satisfied.
  Section FillingOutHypothesesOfAbstractRTATheorem.

First, we prove that in the instantiation of interference and interfering workload, we really take into account everything that can interfere with tsk's jobs, and thus, the scheduler satisfies the abstract notion of work conserving schedule.
    Lemma instantiated_i_and_w_are_coherent_with_schedule:
      work_conserving_ab tsk interference interfering_workload.
    Proof.
      unfold EDF in ×.
      intros j t1 t2 t ARR TSK POS BUSY NEQ; split; intros HYP;
        [move: HYP ⇒ /negP | rewrite scheduled_at_def in HYP; move: HYP ⇒ /eqP HYP ].
      { rewrite negb_or /is_priority_inversion /is_priority_inversion
                /is_interference_from_another_hep_job.
        move ⇒ /andP [HYP1 HYP2].
        ideal_proc_model_sched_case_analysis_eq sched t jo.
        { exfalso; clear HYP1 HYP2.
          eapply instantiated_busy_interval_equivalent_edf_busy_interval in BUSY; eauto 2 with basic_facts.
          move: BUSY ⇒ [PREF _].
            by eapply not_quiet_implies_not_idle; eauto 2 with basic_facts.
        }
        { clear EqSched_jo; move: Sched_jo; rewrite scheduled_at_def; move ⇒ /eqP EqSched_jo.
          rewrite EqSched_jo in HYP1, HYP2.
          move: HYP1 HYP2.
          rewrite Bool.negb_involutive negb_and.
          move ⇒ HYP1 /orP [/negP HYP2| /eqP HYP2].
          - by exfalso.
          - rewrite Bool.negb_involutive in HYP2.
            move: HYP2 ⇒ /eqP /eqP HYP2.
              by subst jo; rewrite scheduled_at_def EqSched_jo.
        }
      }
      { apply/negP;
          rewrite /interference /ideal_jlfp_rta.interference /is_priority_inversion
                  /is_interference_from_another_hep_job
                  HYP negb_or; apply/andP; split.
        - by rewrite Bool.negb_involutive; eapply (EDF_is_reflexive 0).
        - by rewrite negb_and Bool.negb_involutive; apply/orP; right.
      }
    Qed.

Next, we prove that the interference and interfering workload functions are consistent with sequential tasks.
    Lemma instantiated_interference_and_workload_consistent_with_sequential_tasks:
      interference_and_workload_consistent_with_sequential_tasks
        arr_seq sched tsk interference interfering_workload.
    Proof.
      unfold EDF in ×.
      intros j t1 t2 ARR TSK POS BUSY.
      eapply instantiated_busy_interval_equivalent_edf_busy_interval in BUSY; eauto 2 with basic_facts.
      eapply all_jobs_have_completed_equiv_workload_eq_service; eauto 2 with basic_facts.
      intros s INs TSKs.
      rewrite /arrivals_between in INs.
      move: (INs) ⇒ NEQ.
      eapply in_arrivals_implies_arrived_between in NEQ; eauto 2.
      move: NEQ ⇒ /andP [_ JAs].
      move: (BUSY) ⇒ [[ _ [QT [_ /andP [JAj _]]] _]].
      apply QT; try done.
      - eapply in_arrivals_implies_arrived; eauto 2.
      - unfold edf.EDF, EDF; move: TSKs ⇒ /eqP TSKs.
        rewrite /job_deadline /job_deadline_from_task_deadline /hep_job TSK TSKs leq_add2r.
          by apply leq_trans with t1; [apply ltnW | ].
    Qed.

Recall that L is assumed to be a fixed point of the busy interval recurrence. Thanks to this fact, we can prove that every busy interval (according to the concrete definition) is bounded. In addition, we know that the conventional concept of busy interval and the one obtained from the abstract definition (with the interference and interfering workload) coincide. Thus, it follows that any busy interval (in the abstract sense) is bounded.
    Lemma instantiated_busy_intervals_are_bounded:
      busy_intervals_are_bounded_by arr_seq sched tsk interference interfering_workload L.
    Proof.
      unfold EDF in ×.
      intros j ARR TSK POS.
      edestruct exists_busy_interval_from_total_workload_bound
        with (Δ := L) as [t1 [t2 [T1 [T2 GGG]]]]; eauto 2 with basic_facts.
      { by intros; rewrite {2}H_fixed_point; apply total_workload_le_total_rbf''. }
      ∃ t1, t2; split; first by done.
      split; first by done.
        by eapply instantiated_busy_interval_equivalent_edf_busy_interval; eauto 2 with basic_facts.
    Qed.

Next, we prove that IBF is indeed an interference bound.
    Section TaskInterferenceIsBoundedByIBF.

We show that task_interference_is_bounded_by is bounded by IBF by constructing a sequence of inequalities.
      Section Inequalities.

        (* Consider an arbitrary job j of tsk. *)
        Variable j : Job.
        Hypothesis H_j_arrives : arrives_in arr_seq j.
        Hypothesis H_job_of_tsk : job_task j = tsk.
        Hypothesis H_job_cost_positive: job_cost_positive j.

Consider any busy interval t1, t2) of job [j].
        Variable t1 t2 : duration.
        Hypothesis H_busy_interval :
          definitions.busy_interval sched interference interfering_workload j t1 t2.

Let's define A as a relative arrival time of job j (with respect to time t1).
        Let A := job_arrival j - t1.

Consider an arbitrary shift Δ inside the busy interval ...
        Variable Δ : duration.
        Hypothesis H_Δ_in_busy : t1 + Δ < t2.

... and the set of all arrivals between t1 and t1 + Δ.
        Let jobs := arrivals_between arr_seq t1 (t1 + Δ).

Next, we define two predicates on jobs by extending EDF-priority relation.
Predicate EDF_from tsk holds true for any job jo of task tsk such that job_deadline jo ≤ job_deadline j.
        Let EDF_from (tsk : Task) := fun (jo : Job) ⇒ EDF jo j && (job_task jo == tsk).

Predicate EDF_not_from tsk holds true for any job jo such that job_deadline jo ≤ job_deadline j and job_task jo ≠ tsk.
        Let EDF_not_from (tsk : Task) := fun (jo : Job) ⇒ EDF jo j && (job_task jo != tsk).

Recall that IBF(A, R) := priority_inversion_bound + bound_on_total_hep_workload(A, R). The fact that priority_inversion_bound bounds cumulative priority inversion follows from assumption H_priority_inversion_is_bounded.
        Lemma cumulative_priority_inversion_is_bounded:
          cumulative_priority_inversion sched j t1 (t1 + Δ) ≤ priority_inversion_bound.
        Proof.
          unfold priority_inversion_is_bounded_by, EDF in ×.
          apply leq_trans with (cumulative_priority_inversion sched j t1 t2).
          - rewrite [X in _ ≤ X](@big_cat_nat _ _ _ (t1 + Δ)) //=.
            + by rewrite leq_addr.
            + by rewrite /is_priority_inversion leq_addr.
            + by rewrite ltnW.
          - apply H_priority_inversion_is_bounded; try done.
            eapply instantiated_busy_interval_equivalent_edf_busy_interval in H_busy_interval; eauto 2 with basic_facts.
              by move: H_busy_interval ⇒ [PREF _].
        Qed.

Next, we show that bound_on_total_hep_workload(A, R) bounds interference from jobs with higher-or-equal priority.
From lemma instantiated_cumulative_interference_of_hep_tasks_equal_total_interference_of_hep_tasks it follows that cumulative interference from jobs with higher-or-equal priority from other tasks is equal to the total service of jobs with higher-or-equal priority from other tasks. Which in turn means that cumulative interference is bounded by service.
        Lemma cumulative_interference_is_bounded_by_total_service:
          cumulative_interference_from_hep_jobs_from_other_tasks sched j t1 (t1 + Δ)
          ≤ service_of_jobs sched (EDF_not_from tsk) jobs t1 (t1 + Δ).
        Proof.
          move: (H_busy_interval) ⇒ [[/andP [JINBI JINBI2] [QT _]] _].
          erewrite instantiated_cumulative_interference_of_hep_tasks_equal_total_interference_of_hep_tasks;
            eauto 2 with basic_facts.
          - by rewrite -H_job_of_tsk /jobs.
          - rewrite /edf.EDF /EDF instantiated_quiet_time_equivalent_quiet_time //.
            + by apply EDF_is_reflexive.
            + by apply EDF_respects_sequential_tasks.
        Qed.

By lemma service_of_jobs_le_workload, the total service of jobs with higher-or-equal priority from other tasks is at most the total workload of jobs with higher-or-equal priority from other tasks.
        Lemma total_service_is_bounded_by_total_workload:
          service_of_jobs sched (EDF_not_from tsk) jobs t1 (t1 + Δ)
          ≤ workload_of_jobs (EDF_not_from tsk) jobs.
        Proof.
            by apply service_of_jobs_le_workload; eauto 2 with basic_facts.
        Qed.

Next, we reorder summation. So the total workload of jobs with higher-or-equal priority from other tasks is equal to the sum over all tasks tsk_o that are to equal to task tsk of workload of jobs with higher-or-equal priority task tsk_o.
        Lemma reorder_summation:
          workload_of_jobs (EDF_not_from tsk) jobs
          ≤ \sum_(tsk_o <- ts | tsk_o != tsk) workload_of_jobs (EDF_from tsk_o) jobs.
        Proof.
          unfold EDF_from.
          move: (H_busy_interval) ⇒ [[/andP [JINBI JINBI2] [QT _]] _].
          intros.
          rewrite (exchange_big_dep (EDF_not_from tsk)) //=.
          - rewrite /workload_of_jobs big_seq_cond [X in _ ≤ X]big_seq_cond.
            apply leq_sum; move ⇒ jo /andP [ARRo /andP [HEQ TSKo]].
            rewrite (big_rem (job_task jo)) //=.
            rewrite /EDF_from HEQ eq_refl TSKo andTb andTb leq_addr //.
          - eapply H_all_jobs_from_taskset, in_arrivals_implies_arrived; eauto 2.
          - move ⇒ tsko jo /negP NEQ /andP [EQ1 /eqP EQ2].
            rewrite /EDF_not_from EQ1 Bool.andb_true_l; apply/negP; intros CONTR.
            apply: NEQ; clear EQ1.
              by rewrite -EQ2.
        Qed.

Next we focus on one task tsk_o ≠ tsk and consider two cases.
Case 1: Δ ≤ A + ε + D tsk - D tsk_o.
        Section Case1.

Consider an arbitrary task tsk_o ≠ tsk from ts.
          Variable tsk_o : Task.
          Hypothesis H_tsko_in_ts: tsk_o \in ts.
          Hypothesis H_neq: tsk_o != tsk.

And assume that Δ ≤ A + ε + D tsk - D tsk_o.
          Hypothesis H_Δ_le: Δ ≤ A + ε + D tsk - D tsk_o.

Then by definition of rbf, the total workload of jobs with higher-or-equal priority from task tsk_o is bounded rbf(tsk_o, Δ).
          Lemma workload_le_rbf:
            workload_of_jobs (EDF_from tsk_o) jobs ≤ rbf tsk_o Δ.
          Proof.
            unfold workload_of_jobs, EDF_from.
            apply leq_trans with (task_cost tsk_o × number_of_task_arrivals tsk_o t1 (t1 + Δ)).
            { apply leq_trans with (\sum_(j0 <- arrivals_between arr_seq t1 (t1 + Δ) | job_task j0 == tsk_o)
                                     job_cost j0).
              { rewrite big_mkcond [X in _ ≤ X]big_mkcond //= leq_sum //.
                  by intros s _; case (job_task s == tsk_o); case (EDF s j). }
              { rewrite /number_of_task_arrivals /task.arrivals.number_of_task_arrivals
                -sum1_size big_distrr /= big_filter muln1.
                apply leq_sum_seq; move ⇒ jo IN0 /eqP EQ.
                  by rewrite -EQ; apply H_valid_job_cost; apply in_arrivals_implies_arrived in IN0.
              }
            }
            { rewrite leq_mul2l; apply/orP; right.
              rewrite -{2}[Δ](addKn t1).
                by apply H_is_arrival_curve; auto using leq_addr.
            }
          Qed.

        End Case1.

Case 2: A + ε + D tsk - D tsk_o ≤ Δ.
        Section Case2.

Consider an arbitrary task tsk_o ≠ tsk from ts.
          Variable tsk_o : Task.
          Hypothesis H_tsko_in_ts: tsk_o \in ts.
          Hypothesis H_neq: tsk_o != tsk.

And assume that A + ε + D tsk - D tsk_o ≤ Δ.
          Hypothesis H_Δ_ge: A + ε + D tsk - D tsk_o ≤ Δ.

Important step. Next we prove that the total workload of jobs with higher-or-equal priority from task tsk_o over time interval t1, t1 + Δ is bounded by workload over time interval t1, t1 + A + ε + D tsk - D tsk_o. The intuition behind this inequality is that jobs which arrive after time instant t1 + A + ε + D tsk - D tsk_o has smaller priority than job j due to the term D tsk - D tsk_o.
          Lemma total_workload_shorten_range:
            workload_of_jobs (EDF_from tsk_o) (arrivals_between arr_seq t1 (t1 + Δ))
            ≤ workload_of_jobs (EDF_from tsk_o) (arrivals_between arr_seq t1 (t1 + (A + ε + D tsk - D tsk_o))).
          Proof.
            unfold workload_of_jobs, EDF_from.
            move: (H_busy_interval) ⇒ [[/andP [JINBI JINBI2] [QT _]] _].
            set (V := A + ε + D tsk - D tsk_o) in ×.
            rewrite (arrivals_between_cat _ _ (t1 + V)); [ |rewrite leq_addr //|rewrite leq_add2l //].
            rewrite big_cat //=.
            rewrite -[X in _ ≤ X]addn0 leq_add2l leqn0.
            rewrite big_seq_cond.
            apply/eqP; apply big_pred0.
            intros jo; apply/negP; intros CONTR.
            move: CONTR ⇒ /andP [ARRIN /andP [HEP /eqP TSKo]].
            eapply in_arrivals_implies_arrived_between in ARRIN; eauto 2.
            move: ARRIN ⇒ /andP [ARRIN _]; unfold V in ARRIN.
            edestruct (leqP (D tsk_o) (A + ε + D tsk)) as [NEQ2|NEQ2].
            - move: ARRIN; rewrite leqNgt; move ⇒ /negP ARRIN; apply: ARRIN.
              rewrite -(ltn_add2r (D tsk_o)).
              apply leq_ltn_trans with (job_arrival j + D tsk); first by rewrite -H_job_of_tsk -TSKo.
              rewrite addnBA // addnA addnA subnKC // subnK.
              + by rewrite ltn_add2r addn1.
              + apply leq_trans with (A + ε + D tsk); first by done.
                  by rewrite !leq_add2r leq_subr.
            - move: HEP; rewrite /EDF /edf.EDF leqNgt; move ⇒ /negP HEP; apply: HEP.
              apply leq_ltn_trans with (job_arrival jo + (A + D tsk)).
              + rewrite subh1 // addnBA.
                rewrite [in X in _ ≤ X]addnC -addnBA.
                × by rewrite /job_deadline /job_deadline_from_task_deadline H_job_of_tsk leq_addr.
                × by apply leq_trans with (t1 + (A + ε + D tsk - D tsk_o)); first rewrite leq_addr.
                    by apply leq_trans with (job_arrival j); [ | by rewrite leq_addr].
              + rewrite ltn_add2l.
                apply leq_ltn_trans with (A + ε + D tsk).
                × by rewrite leq_add2r leq_addr.
                × by rewrite TSKo.
          Qed.

And similarly to the previous case, by definition of rbf, the total workload of jobs with higher-or-equal priority from task tsk_o is bounded rbf(tsk_o, Δ).
          Lemma workload_le_rbf':
            workload_of_jobs (EDF_from tsk_o) (arrivals_between arr_seq t1 (t1 + (A + ε + D tsk - D tsk_o)))
            ≤ rbf tsk_o (A + ε + D tsk - D tsk_o).
          Proof.
            unfold workload_of_jobs, EDF_from.
            move: (H_busy_interval) ⇒ [[/andP [JINBI JINBI2] [QT _]] _].
            set (V := A + ε + D tsk - D tsk_o) in ×.
            apply leq_trans with
                (task_cost tsk_o × number_of_task_arrivals tsk_o t1 (t1 + (A + ε + D tsk - D tsk_o))).
            - apply leq_trans with
                   (\sum_(jo <- arrivals_between arr_seq t1 (t1 + V) | job_task jo == tsk_o) job_cost jo).
               + rewrite big_mkcond [X in _ ≤ X]big_mkcond //=.
                 rewrite leq_sum //; intros s _.
                   by case (EDF s j).
               + rewrite /number_of_task_arrivals /task.arrivals.number_of_task_arrivals
                 -sum1_size big_distrr /= big_filter.
                 rewrite muln1.
                 apply leq_sum_seq; move ⇒ j0 IN0 /eqP EQ.
                 rewrite -EQ.
                 apply H_valid_job_cost.
                   by apply in_arrivals_implies_arrived in IN0.
            - unfold V in *; clear V.
              set (V := A + ε + D tsk - D tsk_o) in ×.
              rewrite leq_mul2l; apply/orP; right.
              rewrite -{2}[V](addKn t1).
                by apply H_is_arrival_curve; auto using leq_addr.
          Qed.

        End Case2.

By combining case 1 and case 2 we prove that total workload of tasks is at most bound_on_total_hep_workload(A, Δ).
        Corollary sum_of_workloads_is_at_most_bound_on_total_hep_workload :
          \sum_(tsk_o <- ts | tsk_o != tsk) workload_of_jobs (EDF_from tsk_o) jobs
          ≤ bound_on_total_hep_workload A Δ.
        Proof.
          move: (H_busy_interval) ⇒ [[/andP [JINBI JINBI2] [QT _]] _].
          apply leq_sum_seq; intros tsko INtsko NEQT.
          edestruct (leqP Δ (A + ε + D tsk - D tsko)) as [NEQ|NEQ]; [ | apply ltnW in NEQ].
          - move: (NEQ) ⇒ /minn_idPl ⇒ MIN.
            rewrite minnC in MIN; rewrite MIN; clear MIN.
              by apply workload_le_rbf.
          - move: (NEQ) ⇒ /minn_idPr ⇒ MIN.
            rewrite minnC in MIN; rewrite MIN; clear MIN.
            eapply leq_trans. eapply total_workload_shorten_range; eauto 2.
              by eapply workload_le_rbf'; eauto 2.
        Qed.

    End Inequalities.

Recall that in module abstract_seq_RTA hypothesis task_interference_is_bounded_by expects to receive a function that maps some task t, the relative arrival time of a job j of task t, and the length of the interval to the maximum amount of interference.
However, in this module we analyze only one task -- tsk, therefore it is “hard-coded” inside the interference bound function IBF. Therefore, in order for the IBF signature to match the required signature in module abstract_seq_RTA, we wrap the IBF function in a function that accepts, but simply ignores the task.
      Corollary instantiated_task_interference_is_bounded:
        task_interference_is_bounded_by
          arr_seq sched tsk interference interfering_workload (fun tsk A R ⇒ IBF A R).
      Proof.
        unfold EDF in ×.
        intros j R2 t1 t2 ARR TSK N NCOMPL BUSY.
        move: (posnP (@job_cost _ H4 j)) ⇒ [ZERO|POS].
        - exfalso; move: NCOMPL ⇒ /negP COMPL; apply: COMPL.
            by rewrite /completed_by /completed_by ZERO.
        - move: (BUSY) ⇒ [[/andP [JINBI JINBI2] [QT _]] _].
          rewrite (cumulative_task_interference_split arr_seq sched _ _ tsk j);
            eauto 2 with basic_facts; last first.
          { by eapply arrived_between_implies_in_arrivals; eauto. }
          rewrite /I leq_add //.
          + by apply cumulative_priority_inversion_is_bounded with t2.
          + eapply leq_trans. eapply cumulative_interference_is_bounded_by_total_service; eauto 2.
            eapply leq_trans. eapply total_service_is_bounded_by_total_workload; eauto 2.
            eapply leq_trans. eapply reorder_summation; eauto 2.
            eapply leq_trans. eapply sum_of_workloads_is_at_most_bound_on_total_hep_workload; eauto 2.
              by done.
      Qed.

    End TaskInterferenceIsBoundedByIBF.

Finally, we show that there exists a solution for the response-time recurrence.
    Section SolutionOfResponseTimeReccurenceExists.

Consider any job j of tsk.
      Variable j : Job.
      Hypothesis H_j_arrives : arrives_in arr_seq j.
      Hypothesis H_job_of_tsk : job_of_task tsk j.
      Hypothesis H_job_cost_positive : job_cost_positive j.

Given any job j of task tsk that arrives exactly A units after the beginning of the busy interval, the bound of the total interference incurred by j within an interval of length Δ is equal to task_rbf (A + ε) - task_cost tsk + IBF(A, Δ).
      Let total_interference_bound tsk (A Δ : duration) :=
        task_rbf (A + ε) - task_cost tsk + IBF A Δ.

Next, consider any A from the search space (in abstract sense).
      Variable A : duration.
      Hypothesis H_A_is_in_abstract_search_space:
        search_space.is_in_search_space tsk L total_interference_bound A.

We prove that A is also in the concrete search space.
      Lemma A_is_in_concrete_search_space:
        is_in_search_space A.
      Proof.
        move: H_A_is_in_abstract_search_space ⇒ [INSP | [/andP [POSA LTL] [x [LTx INSP2]]]].
        { subst A.
          apply/andP; split; [by done | apply/orP; left].
          rewrite /task_rbf_changes_at neq_ltn; apply/orP; left.
          rewrite /task_rbf /rbf; erewrite task_rbf_0_zero; eauto 2.
          rewrite add0n /task_rbf; apply leq_trans with (task_cost tsk).
          - by eapply leq_trans; eauto 2;
              rewrite -(eqbool_to_eqprop H_job_of_tsk); apply H_valid_job_cost.
          - by eapply task_rbf_1_ge_task_cost; eauto using eqbool_to_eqprop.
        }
        { apply/andP; split; first by done.
          rewrite -[_ || _ ]Bool.negb_involutive negb_or; apply/negP; move ⇒ /andP [/negPn/eqP EQ1 /hasPn EQ2].
          unfold total_interference_bound in × ;apply INSP2.
          rewrite subn1 addn1 prednK // -EQ1.
          apply/eqP; rewrite eqn_add2l eqn_add2l.
          apply: eq_sum_seq; intros tsk_o IN NEQ.
          rewrite addn1 prednK //.
          move: (EQ2 tsk_o IN); clear EQ2;
            rewrite eq_sym NEQ Bool.andb_true_l Bool.negb_involutive; move ⇒ /eqP EQ2.
          edestruct (leqP (A + ε + D tsk - D tsk_o) x) as [CASE|CASE].
          - have ->: minn (A + D tsk - D tsk_o) x = A + D tsk - D tsk_o.
            { rewrite minnE.
              have CASE2: A + D tsk - D tsk_o ≤ x
                by apply leq_trans with (A + ε + D tsk - D tsk_o);
                  first (apply leq_sub2r; rewrite leq_add2r leq_addr).
                by move: CASE2; rewrite -subn_eq0; move ⇒ /eqP CASE2; rewrite CASE2 subn0.
            }
            have ->: minn (A + ε + D tsk - D tsk_o) x = A + ε + D tsk - D tsk_o
              by rewrite minnE; move: CASE; rewrite -subn_eq0; move ⇒ /eqP CASE; rewrite CASE subn0.
              by apply/eqP.
          - have ->: minn (A + D tsk - D tsk_o) x = x.
            { rewrite minnE; rewrite subKn //; rewrite -(leq_add2r 1) !addn1 -subSn.
              + by rewrite -[in X in _ ≤ X]addn1 -addnA [_ + 1]addnC addnA.
              + enough (POS: 0 < A + ε + D tsk - D tsk_o); last eapply leq_ltn_trans with x; eauto 2.
                  by rewrite subn_gt0 -addnA [1 + _]addnC addnA addn1 ltnS in POS.
            }
            have ->: minn (A + ε + D tsk - D tsk_o) x = x by rewrite minnE; rewrite subKn // ltnW.
              by apply/eqP.
        }
      Qed.

Then, there exists solution for response-time recurrence (in the abstract sense).
      Corollary correct_search_space:
        ∃ F,
          A + F = task_rbf (A + ε) - (task_cost tsk - task_run_to_completion_threshold tsk) + IBF A (A + F) ∧
          F + (task_cost tsk - task_run_to_completion_threshold tsk) ≤ R.
      Proof.
        edestruct H_R_is_maximum as [F [FIX NEQ]].
        - by apply A_is_in_concrete_search_space.
        - ∃ F; split; last by done.
          apply/eqP; rewrite {1}FIX.
            by rewrite addnA [_ + priority_inversion_bound]addnC -!addnA.
      Qed.

      End SolutionOfResponseTimeReccurenceExists.

  End FillingOutHypothesesOfAbstractRTATheorem.

Final Theorem

Based on the properties established above, we apply the abstract analysis framework to infer that R is a response-time bound for tsk.
  Theorem uniprocessor_response_time_bound_edf:
    response_time_bounded_by tsk R.
  Proof.
    intros js ARRs TSKs.
    move: (posnP (@job_cost _ H4 js)) ⇒ [ZERO|POS].
    { by rewrite /job_response_time_bound /completed_by ZERO. }
    eapply uniprocessor_response_time_bound_seq with
        (interference0 := interference) (interfering_workload0 := interfering_workload)
        (task_interference_bound_function := fun tsk A R ⇒ IBF A R) (L0 := L); eauto 3.
    - by apply instantiated_i_and_w_are_coherent_with_schedule.
    - by apply instantiated_interference_and_workload_consistent_with_sequential_tasks.
    - by apply instantiated_busy_intervals_are_bounded.
    - by apply instantiated_task_interference_is_bounded.
    - eapply correct_search_space; eauto 2. by apply/eqP.
  Qed.

End AbstractRTAforEDFwithArrivalCurves.